{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T14:12:23Z","timestamp":1772115143093,"version":"3.50.1"},"reference-count":66,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,3,4]],"date-time":"2021-03-04T00:00:00Z","timestamp":1614816000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004837","name":"Ministerio de Ciencia e Innovaci\u00f3n","doi-asserted-by":"publisher","award":["RTI2018-098160-B-I00"],"award-info":[{"award-number":["RTI2018-098160-B-I00"]}],"id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008723","name":"Universidad de C\u00e1diz","doi-asserted-by":"publisher","award":["Ayuda para Estancias en Centros de Investigaci\u00f3n del Programa de Fomento e Impulso de la actividad Investigadora de la Universidad de C\u00e1diz"],"award-info":[{"award-number":["Ayuda para Estancias en Centros de Investigaci\u00f3n del Programa de Fomento e Impulso de la actividad Investigadora de la Universidad de C\u00e1diz"]}],"id":[{"id":"10.13039\/501100008723","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This study aims to produce accurate predictions of the NO2 concentrations at a specific station of a monitoring network located in the Bay of Algeciras (Spain). Artificial neural networks (ANNs) and sequence-to-sequence long short-term memory networks (LSTMs) were used to create the forecasting models. Additionally, a new prediction method was proposed combining LSTMs using a rolling window scheme with a cross-validation procedure for time series (LSTM-CVT). Two different strategies were followed regarding the input variables: using NO2 from the station or employing NO2 and other pollutants data from any station of the network plus meteorological variables. The ANN and LSTM-CVT exogenous models used lagged datasets of different window sizes. Several feature ranking methods were used to select the top lagged variables and include them in the final exogenous datasets. Prediction horizons of t + 1, t + 4 and t + 8 were employed. The exogenous variables inclusion enhanced the model\u2019s performance, especially for t + 4 (\u03c1 \u2248 0.68 to \u03c1 \u2248 0.74) and t + 8 (\u03c1 \u2248 0.59 to \u03c1 \u2248 0.66). The proposed LSTM-CVT method delivered promising results as the best performing models per prediction horizon employed this new methodology. Additionally, per each parameter combination, it obtained lower error values than ANNs in 85% of the cases.<\/jats:p>","DOI":"10.3390\/s21051770","type":"journal-article","created":{"date-parts":[[2021,3,5]],"date-time":"2021-03-05T00:39:07Z","timestamp":1614904747000},"page":"1770","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Artificial Neural Networks, Sequence-to-Sequence LSTMs, and Exogenous Variables as Analytical Tools for NO2 (Air Pollution) Forecasting: A Case Study in the Bay of Algeciras (Spain)"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5765-369X","authenticated-orcid":false,"given":"Javier","family":"Gonz\u00e1lez-Enrique","sequence":"first","affiliation":[{"name":"Intelligent Modelling of Systems Research Group (MIS), Department of Computer Science Engineering, Polytechnic School of Engineering, University of C\u00e1diz, 11204 Algeciras, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2170-0693","authenticated-orcid":false,"given":"Juan Jes\u00fas","family":"Ruiz-Aguilar","sequence":"additional","affiliation":[{"name":"Intelligent Modelling of Systems Research Group (MIS), Department of Industrial and Civil Engineering, Polytechnic School of Engineering, University of C\u00e1diz, 11204 Algeciras, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jos\u00e9 Antonio","family":"Moscoso-L\u00f3pez","sequence":"additional","affiliation":[{"name":"Intelligent Modelling of Systems Research Group (MIS), Department of Industrial and Civil Engineering, Polytechnic School of Engineering, University of C\u00e1diz, 11204 Algeciras, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2662-798X","authenticated-orcid":false,"given":"Daniel","family":"Urda","sequence":"additional","affiliation":[{"name":"Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingenier\u00eda Inform\u00e1tica, Escuela Polit\u00e9cnica Superior, Universidad de Burgos, Av. Cantabria s\/n, 09006 Burgos, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lipika","family":"Deka","sequence":"additional","affiliation":[{"name":"The De Montfort University Interdisciplinary Group in Intelligent Transport Systems (DIGITS), Department of Computer Science and Informatics, De Montfort University, Leicester LE1 9BH, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ignacio J.","family":"Turias","sequence":"additional","affiliation":[{"name":"Intelligent Modelling of Systems Research Group (MIS), Department of Computer Science Engineering, Polytechnic School of Engineering, University of C\u00e1diz, 11204 Algeciras, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"596","DOI":"10.1164\/rccm.200906-0858OC","article-title":"Traffic-related Air Pollution and the Development of Asthma and Allergies during the First 8 Years of Life","volume":"181","author":"Gehring","year":"2010","journal-title":"Am. J. Respir. Crit. Care Med."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Lau, N., Norman, A., Smith, M.J., Sarkar, A., and Gao, Z. (2018). Association between Traffic Related Air Pollution and the Development of Asthma Phenotypes in Children: A Systematic Review. Int. J. Chronic Dis., 2018.","DOI":"10.1155\/2018\/4047386"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"9195","DOI":"10.1016\/j.atmosenv.2007.07.057","article-title":"Analysis of air quality within a street canyon using statistical and dispersion modelling techniques","volume":"41","author":"Westmoreland","year":"2007","journal-title":"Atmos. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2967","DOI":"10.1016\/j.atmosenv.2006.12.013","article-title":"Two-days ahead prediction of daily maximum concentrations of SO2, O3, PM10, NO2, CO in the urban area of Palermo, Italy","volume":"41","author":"Brunelli","year":"2007","journal-title":"Atmos. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1186\/2190-4715-24-21","article-title":"Primary NO2 emissions and their impact on air quality in traffic environments in Germany","volume":"24","author":"Kurtenbach","year":"2012","journal-title":"Environ. Sci. Eur."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Finlayson-Pitts, B.J., and Pitts, J.N.J. (2000). The Atmospheric System. Chemistry of the Upper and Lower Atmosphere: Theory, Experiments, and Applications, Academic Press.","DOI":"10.1016\/B978-012257060-5\/50004-6"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Jiao, Y., Wang, Z., and Zhang, Y. (2019, January 24\u201326). Prediction of Air Quality Index Based on LSTM. Proceedings of the 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), Chongqing, China.","DOI":"10.1109\/ITAIC.2019.8785602"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"744","DOI":"10.1183\/09031936.00114713","article-title":"Nitrogen dioxide and mortality: Review and meta-analysis of long-term studies","volume":"44","author":"Faustini","year":"2014","journal-title":"Eur. Respir. J."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Seinfeld, J.H., and Pandis, S.N. (1998). Atmospheric Chemistry and Physics: From Air Pollution to Climate Change, John Wiley & Sons.","DOI":"10.1063\/1.882420"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1016\/j.envsoft.2007.04.001","article-title":"A deterministic air quality forecasting system for Torino urban area, Italy","volume":"23","author":"Finardi","year":"2008","journal-title":"Environ. Model. Softw."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.envsoft.2016.02.030","article-title":"Air pollution prediction via multi-label classification","volume":"80","author":"Corani","year":"2016","journal-title":"Environ. Model. Softw."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2068","DOI":"10.1016\/j.atmosenv.2005.11.041","article-title":"Statistical models for the prediction of respirable suspended particulate matter in urban cities","volume":"40","author":"Goyal","year":"2006","journal-title":"Atmos. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.trd.2017.07.009","article-title":"Enhanced transport-related air pollution prediction through a novel metamodel approach","volume":"55","author":"Catalano","year":"2017","journal-title":"Transp. Res. Part D Transp. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"116885","DOI":"10.1016\/j.atmosenv.2019.116885","article-title":"Improving air quality prediction accuracy at larger temporal resolutions using deep learning and transfer learning techniques","volume":"214","author":"Ma","year":"2019","journal-title":"Atmos. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1016\/S1352-2310(98)00230-1","article-title":"Neural network modelling and prediction of hourly NOx and NO2 concentrations in urban air in London","volume":"33","author":"Gardner","year":"1999","journal-title":"Atmos. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1016\/S1352-2310(00)00385-X","article-title":"Neural networks and periodic components used in air quality forecasting","volume":"35","author":"Kolehmainen","year":"2001","journal-title":"Atmos. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/S0304-3800(01)00434-3","article-title":"Atmospheric urban pollution: Applications of an artificial neural network (ANN) to the city of Perugia","volume":"148","author":"Viotti","year":"2002","journal-title":"Ecol. Model."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4539","DOI":"10.1016\/S1352-2310(03)00583-1","article-title":"Extensive evaluation of neural network models for the prediction of NO2 and PM10 concentrations, compared with a deterministic modelling system and measurements in central Helsinki","volume":"37","author":"Kukkonen","year":"2003","journal-title":"Atmos. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1016\/j.envsoft.2004.07.008","article-title":"Regression and multilayer perceptron-based models to forecast hourly O3 and NO2 levels in the Bilbao area","volume":"21","author":"Madariaga","year":"2006","journal-title":"Environ. Model. Softw."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"751","DOI":"10.1007\/s00477-009-0361-8","article-title":"ARIMA forecasting of ambient air pollutants (O3, NO, NO2 and CO)","volume":"24","author":"Kumar","year":"2010","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_21","first-page":"59","article-title":"Suhartono Forecasting of Air Pollution Index with Artificial Neural Network","volume":"63","author":"Rahman","year":"2013","journal-title":"J. Teknol. (Sci. Eng.)"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1016\/j.apr.2016.01.004","article-title":"Air pollutants concentrations forecasting using back propagation neural network based on wavelet decomposition with meteorological conditions","volume":"7","author":"Bai","year":"2016","journal-title":"Atmos. Pollut. Res."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Mart\u00ednez \u00c1lvarez, F., Troncoso Lora, A., S\u00e1ez Mu\u00f1oz, J.A., Quinti\u00e1n, H., and Corchado, E. (2019, January 13\u201315). A Hybrid Approach for Short-Term NO2 Forecasting: Case Study of Bay of Algeciras (Spain). Proceedings of the 14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019), Seville, Spain.","DOI":"10.1007\/978-3-030-20055-8"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2451","DOI":"10.1162\/089976600300015015","article-title":"Learning to forget: Continual prediction with LSTM","volume":"12","author":"Gers","year":"2000","journal-title":"Neural Comput."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"K\u00f6k, I., \u015eim\u015fek, M.U., and \u00d6zdemir, S. (2017, January 11\u201314). A deep learning model for air quality prediction in smart cities. Proceedings of the 2017 IEEE International Conference on Big Data (Big Data), Boston, MA, USA.","DOI":"10.1109\/BigData.2017.8258144"},{"key":"ref_26","unstructured":"Vicente, J.M.F., \u00c1lvarez-S\u00e1nchez, J.R., L\u00f3pez, F.d.l.P., Moreo, J.T., and Adeli, H. (2017). Air Quality Forecasting in Madrid Using Long Short-Term Memory Networks. Biomedical Applications Based on Natural and Artificial Computing. IWINAC 2017. Lecture Notes in Computer Science, Vol 10338, Springer."},{"key":"ref_27","first-page":"18","article-title":"Air Quality Prediction in Visakhapatnam with LSTM based Recurrent Neural Networks","volume":"11","author":"Rao","year":"2019","journal-title":"Int. J. Intell. Syst. Appl."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"12935","DOI":"10.5194\/acp-19-12935-2019","article-title":"Development of daily PM10 and PM2.5 prediction system using a deep long short-term memory neural network model","volume":"19","author":"Kim","year":"2019","journal-title":"Atmos. Chem. Phys."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3437","DOI":"10.1039\/c1em10303b","article-title":"An integrated air quality forecast system for a metropolitan area","volume":"13","author":"Carnevale","year":"2011","journal-title":"J. Environ. Monit."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1002\/lom3.10231","article-title":"Water renewal in semi-enclosed basins: A high resolution Lagrangian approach with application to the Bay of Algeciras, Strait of Gibraltar","volume":"16","author":"Sammartino","year":"2018","journal-title":"Limnol. Oceanogr. Methods"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1007\/s11157-010-9227-2","article-title":"Air quality indices: A review","volume":"10","author":"Plaia","year":"2011","journal-title":"Rev. Environ. Sci. Biotechnol."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Rumelhart, D.E., and McClelland, J.L. (1986). Learning internal representations by error propagation. Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 1. Foundations, MIT Press.","DOI":"10.7551\/mitpress\/5236.001.0001"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/0893-6080(89)90020-8","article-title":"Multilayer feedforward networks are universal approximators","volume":"2","author":"Hornik","year":"1989","journal-title":"Neural Netw."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Bishop, C.M. (1995). Neural Networks for Pattern Recognition, Oxford University Press, Inc.","DOI":"10.1093\/oso\/9780198538493.001.0001"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2627","DOI":"10.1016\/S1352-2310(97)00447-0","article-title":"Artificial neural networks (the multilayer perceptron)\u2014A review of applications in the atmospheric sciences","volume":"32","author":"Gardner","year":"1998","journal-title":"Atmos. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1016\/S0893-6080(05)80056-5","article-title":"A scaled conjugate gradient algorithm for fast supervised learning","volume":"6","year":"1993","journal-title":"Neural Netw."},{"key":"ref_37","unstructured":"Sarle, W.S. (1995, January 21\u201324). Stopped Training and Other Remedies for Overfitting. Proceedings of the 27th Symposium on the Interface of Computing Science and Statistics, Pittsburgh, PA, USA."},{"key":"ref_38","first-page":"856","article-title":"A Genetic Algorithm and Neural Network Stacking Ensemble Approach to Improve NO2 Level Estimations","volume":"Volume 11506","author":"Rojas","year":"2019","journal-title":"Proceedings of the Advances in Computational Intelligence, IWANN 2019"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1007\/s10661-019-7901-6","article-title":"An artificial neural network ensemble approach to generate air pollution maps","volume":"191","author":"Turias","year":"2019","journal-title":"Environ. Monit. Assess."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1007\/s00477-018-01644-0","article-title":"Spatial and meteorological relevance in NO2 estimations. A case study in the Bay of Algeciras (Spain)","volume":"33","author":"Turias","year":"2019","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ruiz-Aguilar, J.J., Turias, I., Gonz\u00e1lez-Enrique, J., Urda, D., and Elizondo, D. (2020). A permutation entropy-based EMD\u2013ANN forecasting ensemble approach for wind speed prediction. Neural Comput. Appl.","DOI":"10.1007\/s00521-020-05141-w"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1409","DOI":"10.1007\/s00477-013-0827-6","article-title":"Prediction of PM10 and SO2 exceedances to control air pollution in the Bay of Algeciras, Spain","volume":"28","author":"Turias","year":"2014","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1142\/S0218488598000094","article-title":"The vanishing gradient problem during learning recurrent neural nets and problem solutions","volume":"6","author":"Hochreiter","year":"1998","journal-title":"Int. J. Uncertain. Fuzziness Knowl.-Based Syst."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1109\/72.279181","article-title":"Learning long-term dependencies with gradient descent is difficult","volume":"5","author":"Bengio","year":"1994","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"866","DOI":"10.1080\/10962247.2018.1459956","article-title":"Forecasting air quality time series using deep learning","volume":"68","author":"Freeman","year":"2018","journal-title":"J. Air Waste Manag. Assoc."},{"key":"ref_47","first-page":"802","article-title":"Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting","volume":"Volume 2018","author":"Shi","year":"2015","journal-title":"Proceedings of the 28th International Conference on Neural Information Processing Systems\u2014Volume 1"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Brockwell, P.J., Brockwell, P.J., Davis, R.A., and Davis, R.A. (2002). Introduction to Time Series and Forecasting, Springer.","DOI":"10.1007\/b97391"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.ins.2011.12.028","article-title":"On the use of cross-validation for time series predictor evaluation","volume":"191","author":"Bergmeir","year":"2012","journal-title":"Inf. Sci."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1111\/j.2517-6161.1974.tb00994.x","article-title":"Cross-Validatory Choice and Assessment of Statistical Predictions","volume":"36","author":"Stone","year":"1974","journal-title":"J. R. Stat. Soc. Ser. B"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1214\/09-SS054","article-title":"A survey of cross-validation procedures for model selection","volume":"4","author":"Arlot","year":"2010","journal-title":"Stat. Surv."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.csda.2014.02.001","article-title":"On the usefulness of cross-validation for directional forecast evaluation","volume":"76","author":"Bergmeir","year":"2014","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","article-title":"A Mathematical Theory of Communication","volume":"27","author":"Shannon","year":"1948","journal-title":"Bell Syst. Tech. J."},{"key":"ref_54","first-page":"283","article-title":"Information Theoretical Estimators Toolbox","volume":"15","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_55","unstructured":"Ding, A.A., and Li, Y. (2013). Copula Correlation: An Equitable Dependence Measure and Extension of Pearson\u2019s Correlation. arXiv."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"6662","DOI":"10.1038\/srep06662","article-title":"A Novel Algorithm for the Precise Calculation of the Maximal Information Coefficient","volume":"4","author":"Zhang","year":"2014","journal-title":"Sci. Rep."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1093\/bioinformatics\/bts707","article-title":"Minerva and minepy: A C engine for the MINE suite and its R, Python and MATLAB wrappers","volume":"29","author":"Albanese","year":"2013","journal-title":"Bioinformatics"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1226","DOI":"10.1109\/TPAMI.2005.159","article-title":"Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy","volume":"27","author":"Peng","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1002\/int.21833","article-title":"Fast-mRMR: Fast Minimum Redundancy Maximum Relevance Algorithm for High-Dimensional Big Data","volume":"32","author":"Lastra","year":"2017","journal-title":"Int. J. Intell. Syst."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1080\/02723646.1981.10642213","article-title":"On the validation of models","volume":"2","author":"Willmott","year":"1981","journal-title":"Phys. Geogr."},{"key":"ref_61","first-page":"3104","article-title":"Sequence to Sequence Learning with Neural Networks","volume":"Volume 2","author":"Sutskever","year":"2014","journal-title":"Proceedings of the 27th International Conference on Neural Information Processing Systems"},{"key":"ref_62","first-page":"2951","article-title":"Practical Bayesian Optimization of Machine Learning Algorithms","volume":"Volume 25","author":"Pereira","year":"2012","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"ref_63","unstructured":"Zhang, N.L., and Tian, J. (2014, January 23\u201327). Bayesian optimization with unknown constraints. Proceedings of the Uncertainty in Artificial Intelligence\u2014Proceedings of the 30th Conference, UAI 2014, Quebec City, QC, Canada."},{"key":"ref_64","first-page":"1929","article-title":"Dropout: A Simple Way to Prevent Neural Networks from Overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1080\/01621459.1937.10503522","article-title":"The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance","volume":"32","author":"Friedman","year":"1937","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Hochberg, Y., and Tamhane, A.C. (1987). Multiple Comparison Procedures, John Wiley & Sons, Inc.","DOI":"10.1002\/9780470316672"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/5\/1770\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:32:38Z","timestamp":1760160758000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/5\/1770"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,4]]},"references-count":66,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2021,3]]}},"alternative-id":["s21051770"],"URL":"https:\/\/doi.org\/10.3390\/s21051770","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,4]]}}}